不完全信息和费用约束下的序贯抽样自适应策略
Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint
摘要：
我们考虑的问题，序贯抽样从一个有限数量的独立的统计总体，以最大限度地提高期望的无限地平线平均结果每一个时期，在约束下，期望的平均抽样成本不超过一个上限。结果分布未知。我们构造了一类相容的自适应策略，在此策略下，对于所有具有有限均值的分布，在完全信息下，平均结果以概率1收敛到真值。我们还比较了收敛速度的各种政策在这一类使用模拟。
英文摘要：
We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
